runScanpyUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object

runScanpyUMAP(
  inSCE,
  useAssay = NULL,
  useReducedDim = "scanpyPCA",
  reducedDimName = "scanpyUMAP",
  dims = 40,
  minDist = 0.5,
  nNeighbors = 10,
  spread = 1,
  alpha = 1,
  gamma = 1,
  externalReduction = NULL,
  seed = 12345
)

Arguments

inSCE: (sce) object on which to compute the UMAP
useAssay: Specify name of assay to use. Default is NULL, so useReducedDim param will be used instead.
useReducedDim: Reduction to use for computing UMAP. Default is "scanpyPCA".
reducedDimName: Name of new reducedDims object containing Scanpy UMAP Default scanpyUMAP.
dims: Numerical value of how many reduction components to use for UMAP computation. Default 40.
minDist: Sets the "min_dist" parameter to the underlying UMAP call. Default 0.5.
nNeighbors: Sets the "n_neighbors" parameter to the underlying UMAP call. Default 10.
spread: Sets the "spread" parameter to the underlying UMAP call. Default 1.
alpha: Sets the "alpha" parameter to the underlying UMAP call. Default 1.
gamma: Sets the "gamma" parameter to the underlying UMAP call. Default 1.
externalReduction: Pass DimReduce object if PCA computed through other libraries. Default NULL.
seed: Specify numeric value to set as a seed. Default 12345.

Value

Updated sce object with UMAP computations stored

Examples

data(scExample, package = "singleCellTK")
if (FALSE) { # \dontrun{
sce <- runScanpyNormalizeData(sce, useAssay = "counts")
sce <- runScanpyFindHVG(sce, useAssay = "scanpyNormData", method = "seurat")
sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")
sce <- runScanpyPCA(sce, useAssay = "scanpyScaledData")
sce <- runScanpyFindClusters(sce, useReducedDim = "scanpyPCA")
sce <- runScanpyUMAP(sce, useReducedDim = "scanpyPCA")
} # }